Integrations

Integrations are Kubernetes components and external tools that can be used with NVIDIA Run:ai for development, training, orchestration, data access, and monitoring.

Integrations fall into two support levels:

• Supported integrations (out of the box) - NVIDIA Run:ai includes built-in support and documentation. You may still need cluster-side installation (for example, install an operator and its CRDs) before you can use the integration.

• Community Support integrations - Not supported out of the box, but commonly used with prior customer support experience and reference guides.

Supported Integrations

Frameworks

Framework	Category	Supported Version	Additional Information
Dynamo operator	Distributed inference	0.7.0	Dynamo operator is a Kubernetes operator that simplifies the deployment, configuration, and lifecycle management of DynamoGraphs. NVIDIA Run:ai provides out of the box support for submitting Dynamo workloads via YAML. See Dynamo operator documentation for more details.
NIM operator	Model Serving	3.0.x	The NVIDIA NIM Operator enables Kubernetes cluster administrators to operate the software components and services necessary to deploy NVIDIA NIMs and NVIDIA NeMo microservices in Kubernetes. NVIDIA Run:ai provides out of the box support for submitting NIM operator workloads via YAML. See NIM operator documentation for more details.
LeaderWorkerSet (LWS)	Distributed inference	0.6.0 or higher	NVIDIA Run:ai provides out of the box support for submitting LWS workloads via YAML and NVIDIA Run:ai native distributed inference workloads using LWS via API.
Kubeflow MPI	Distributed training	MPI Operator v0.6.0 or higher	NVIDIA Run:ai provides out of the box support for submitting MPI workloads via API, CLI or UI. See Distributed training for more details.
PyTorch	Distributed training	Kubeflow Training Operator v1.9.2	NVIDIA Run:ai provides out of the box support for submitting PyTorch workloads via API, CLI or UI. See Distributed training for more details.
TensorFlow	Distributed training	Kubeflow Training Operator v1.9.2	NVIDIA Run:ai provides out of the box support for submitting TensorFlow workloads via API, CLI or UI. See Distributed training for more details.
XGBoost	Distributed training	Kubeflow Training Operator v1.9.2	NVIDIA Run:ai provides out of the box support for submitting XGBoost via API, CLI or UI. See Distributed training for more details.
JAX	Distributed training	Kubeflow Training Operator v1.9.2	NVIDIA Run:ai provides out of the box support for submitting JAX workloads via API, CLI or UI. See Distributed training for more details.
Triton	Orchestration	Any version	Usage via docker base image

Development Tools

Tool	Category	Additional Information
Jupyter Notebook	Development	NVIDIA Run:ai provides integrated support with Jupyter Notebooks. See Jupyter Notebook quick start example.
PyCharm	Development	Containers created by NVIDIA Run:ai can be accessed via PyCharm.
VScode	Development	Containers created by NVIDIA Run:ai can be accessed via Visual Studio Code. You can automatically launch Visual Studio code web from the NVIDIA Run:ai console.

Storage and Registries

Tool	Category	Additional Information
Docker Registry	Repositories	NVIDIA Run:ai allows using a docker registry as a Credential asset
GitHub	Storage	NVIDIA Run:ai communicates with GitHub by defining it as a data source asset
S3	Storage	NVIDIA Run:ai communicates with S3 by defining a data source asset

Experiment Tracking and Monitoring

Tool	Category	Additional Information
TensorBoard	Experiment tracking	NVIDIA Run:ai comes with a preset TensorBoard Environment asset

Infrastructure and Cost Optimization

Tool	Category	Additional Information
Karpenter	Cost Optimization	NVIDIA Run:ai provides out of the box support for Karpenter to save cloud costs. Integration notes with Karpenter can be found here.

Community Support Integrations

Our Customer Success team has prior experience assisting customers with setup. In many cases, the NVIDIA Enterprise Support Portal may include additional reference documentation provided on an as-is basis.

Tool	Category	Additional Information
Apache Airflow	Orchestration	It is possible to schedule Airflow workflows with the NVIDIA Run:ai Scheduler. Sample code: How to integrate NVIDIA Run:ai with Apache Airflow.
Argo workflows	Orchestration	It is possible to schedule Argo workflows with the NVIDIA Run:ai Scheduler. Sample code: How to integrate NVIDIA Run:ai with Argo Workflows.
ClearML	Experiment tracking	It is possible to schedule ClearML workloads with the NVIDIA Run:ai Scheduler.
JupyterHub	Development	It is possible to submit NVIDIA Run:ai workloads via JupyterHub.
Kubeflow notebooks	Development	It is possible to launch a Kubeflow notebook with the NVIDIA Run:ai Scheduler. Sample code: How to integrate NVIDIA Run:ai with Kubeflow.
Kubeflow Pipelines	Orchestration	It is possible to schedule kubeflow pipelines with the NVIDIA Run:ai Scheduler. Sample code: How to integrate NVIDIA Run:ai with Kubeflow.
MLFlow	Model Serving	It is possible to use ML Flow together with the NVIDIA Run:ai Scheduler.
Ray	Training, inference, data processing	It is possible to schedule Ray jobs with the NVIDIA Run:ai Scheduler. Sample code: How to Integrate NVIDIA Run:ai with Ray.
SeldonX	Orchestration	It is possible to schedule Seldon Core workloads with the NVIDIA Run:ai Scheduler.
Spark	Orchestration	It is possible to schedule Spark workflows with the NVIDIA Run:ai Scheduler.
Weights & Biases	Experiment tracking	It is possible to schedule W&B workloads with the NVIDIA Run:ai Scheduler. Sample code: How to integrate with Weights and Biases.